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Compensation Mechanisms for Double Auctions in Peer-to-Peer Local Energy Markets

Current Sustainable/Renewable Energy Reports 2020年第4期7卷 165-175页

作者： Gerwin, Christoph Mieth, Robert Dvorkin, Yury Workgroup for Infrastructure Policy Technische Universität Berlin Straße des 17. Juni 135 Berlin10623 Germany Tandon School of Engineering Department of Electrical and Computer Engineering NYU BrooklynNY11201 United States Control Systems Group Technische Universität Berlin Einsteinufer 17 Berlin10587 Germany

Purpose of Review: This paper compares different double-auction pricing mechanisms in the context of peer-to-peer trading in local electricity markets. Benchmarking against the traditional system marginal price (SMP) approach, we analyze the outcomes of the Vickrey-Clarke-Groves (VCG) and Pay-as-bid (PAB) approaches, which lead to a revenue imbalance in the market. To mitigate this imbalance, we propose a set of mechanisms that impose trading fees or subsidies with different fairness policies and discuss their impact on the behavior of market participants. Recent Findings: Proliferation of small-scale and distributed energy resources in low-voltage distribution systems has spurred the development of platforms for peer-to-peer local electricity trading. While recent studies prove that local electricity trading can be beneficial to both the peers and system, implementation of local energy markets is still in the early stages and there is no consensus on an optimal market design. Summary: This paper provides a detailed description of market properties for a competitive LEM that is cleared using the SMP, VCG, and PAB approaches. We show that the proposed compensation mechanisms effectively enforce predefined fairness policies and derive exact payments for each peer depending on market results and individual peer characteristics. The case study uses real-world data to illustrate that different compensation mechanisms can be used to incentivize desirable peer behavior, e.g., installation of new distributed energy resources, investment in grid supportive generation, or flexible energy usage. © 2020, Springer Nature Switzerland AG.

关键词： Power markets

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Adolescent Asthma Monitoring: A Preliminary Study of Audio and Spirometry Modalities

Adolescent Asthma Monitoring: A Preliminary Study of Audio a...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jeffrey A. Barahona Katie Mills Michelle Hernandez Alper Bozkurt Delesha Carpenter Edgar J. Lobaton Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA Children’s Research Institute University of North Carolina at Chapel Hill Chapel Hill NC USA Division of Pharmaceutical Outcomes and Policy Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel Hill NC USA

Asthma patients’ sleep quality is correlated with how well their asthma symptoms are controlled. In this paper, deep learning techniques are explored to improve forecasting of forced expiratory volume in one second (FEV1) by using audio data from participants and test whether auditory sleep disturbances are correlated with poorer asthma outcomes. These are applied to a representative data set of FEV1 collected from a commercially available sprirometer and audio spectrograms collected overnight using a smartphone. A model for detecting nonverbal vocalizations including coughs, sneezes, sighs, snoring, throat clearing, sniffs, and breathing sounds was trained and used to capture nightly sleep disturbances. Our preliminary analysis found significant improvement in FEV1 forecasting when using overnight nonverbal vocalization detections as an additional feature for regression using XGBoost over using only spirometry *** relevance— This preliminary study establishes up to 30% improvement of FEV1 forecasting using features generated by deep learning techniques over only spirometry-based features.

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Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults

Cluster Analysis to Find Temporal Physical Activity Patterns...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Jiaqi Guo Marah M. Aqeel Luotao Lin Saul B. Gelfand Heather A. Eicher-Miller Anindya Bhadra Erin Hennessy Elizabeth A. Richards Edward J. Delp School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Nutrition Science Purdue University West Lafayette IN USA Department of Statistics Purdue University West Lafayette IN USA Friedman School of Nutrition Science and Policy Tufts University Boston MA USA School of Nursing Purdue University West Lafayette IN USA

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach to cluster the daily physical activity time series from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). The clusters are used to find temporal physical activity patterns among US adults. A number of distance measures and distance-based clustering methods are investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

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Uniform Overlap Feeding Network To Reduce the Number of Phase Shifters in Scan Arrays

Uniform Overlap Feeding Network To Reduce the Number of Phas...

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2022 International Symposium on Antennas and Propagation (ISAP)

作者： Maryam Shadi Mohammad Reza Tavakol Mohammad Alibakhshikenari Farhad Arpanaei Francisco Falcone Dept. of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Dept. of Electrical Engineering Sharif University of Technology Tehran Iran Dept. of Signal Theory and Communications Universidad Carlos III de Madrid Madrid Spain Dept. of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain Dept. of Electric Electronic and comm. Eng. Public University of Navarre Pamplona Spain

ISBN: (数字)9781665479622

ISBN: (纸本)9781665479639

A new phased array feeding network that minimizes the number of phase shifters over a limited scan is illustrated. The presented concept groups the primary phased-array elements into sequences of uniform overlap sub-arrays that employ a single phase-shifter for each sub-array. Besides, using overlapped elements reduces the distance between secondary array elements and increases the array’s gain. The proposed uniform overlap feeding network consists of some Wilkinson power dividers and T-junction combiners to excitation microstrip array antenna in 28GHz, simulated in the CST. The 16-elements linear array antenna uses seven phase-shifter (reducing more than 50% in the number of phase shifters) to scan up to 30° with SLL lower than 15dB. As reported, all input ports have a good matching, lower than 15dB, and high isolation around 25dB.

关键词： Phased arrays Power dividers Microstrip antenna arrays Phase shifters Microstrip antennas Antenna feeds Millimeter wave propagation

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Modelling of the low voltage distribution network for the integration of distributed generation and charging stations for electric vehicles 6

Modelling of the low voltage distribution network for the in...

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6th International Conference on Smart and Sustainable Technologies, SpliTech 2021

作者： Raff, Rebeka Vukovic, Mato Topic, Danijel Cimic, Zvonimir Public Institution Development Agency of Osijek-Baranja County Osijek Croatia HEP ODS D.o.o.. Elektra Požega Požega Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek J.J. Strossmayer University of Osijek Osijek Croatia

ISBN: (纸本)9789532901122

In this paper modelling of the low voltage distribution network for the integration of the distributed generation and charging stations for electric vehicles (EV) is presented. The impact of EV charging stations on the electric power system is discussed. Modelling of the influence of the prosumers, distributed generation and EV's charging stations on the radial distribution network using NEPLAN package is presented. Results of simulations for scenarios A, B, C and D are discussed and presented through diagrams and tables. Tables show different parameters which indicate the impact of different scenarios on the radial distribution network. © 2021 University of Split, FESB.

关键词： Distributed power generation

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Evaluation Method of Ocean Healing Content on Stress Relieve Effects by Using Smart Watch and Questionnaire

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Journal of Coastal Research 2024年第SP1期116卷 493-497页

作者： Sunghoon Hong Hak Soo Lim Choong Gon Kim Jin Hyeok Park Min Gyu Kim Gi Young Chae Go Eun Sim Seon Woo Bak Ju Hwan Kim Oh Joon Kwon Coastal Disaster & Safety Research Department Korea Institute of Ocean Science and Technology Busan Republic of Korea Ocean Climate Response & Ecosystem Research Department Korea Institute of Ocean Science and Technology Busan Republic of Korea Ocean Policy Research Center Korea Institute of Ocean Science and Technology Busan Republic of Korea Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Republic of Korea DM Studio Busan Republic of Korea

Hong, S.; Lim, H.S.; Kim, C.G.; Park, J.H.; Kim, M.G.; Chae, G.Y.; Sim, G.E.; Bak, S.W.; Kim, J.H., and Kwon, O.J., 2023. Evaluation method of ocean healing content on stress relieve effects by using smart watch and questionnaire. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 493-497. Charlotte (North Carolina), ISSN ***, research on ocean healing resources has been conducted widely to enhance physical and mental health conditions. Among the ocean healing resources, peaceful and beautiful marine sceneries are also regarded as natural resources that can relieve stress and body pain. In this paper, the positive effects of immersive ocean healing content on stress mitigation are investigated. The basic evaluation procedure with specific biometric data was also established. To investigate this effect, a realistic ocean healing content with stereoscopic visualization is developed and tested as a preliminary experiment. During the experiments, biometric data (blood pressure, heart rate, stress index) are acquired using a wearable Smart Watch (Galaxy Watch 5 Pro) before and after watching the ocean healing content. In addition, the questionnaire evaluation of variation of SRI (Stress Response Inventory) is included. The average stress index value decreased -15.3% after watching ocean healing content, whereas there were no significant changes in blood pressure and heart beat. In addition, the prominent enhancement of SRI value in all stress properties (tension, aggression, somatization, anger, depression, fatigue, frustration) was found. Even though the number of experiment participants was limited, this study presents the positive roles of ocean healing content that improves mental health promotion effects. Moreover, this ocean healing evaluation methodology can be utilized and reviewed as preli

关键词： immersive content marine scenery mental health Ocean healing wearable device linkage technology

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Machine learning approaches and databases for prediction of drug-target interaction: a survey paper (vol 22, pg 247, 2021)

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BRIEFINGS IN BIOINFORMATICS 2021年第1期22卷 606-606页

作者： Bagherian, Maryam Sabeti, Elyas Wang, Kai Sartor, Maureen A. Nikolovska-Coleska, Zaneta Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 USA Michigan Institute for Data Science University of Michigan Ann Arbor MI 48109 USA Department of Biostatistics School of Public Health University of Michigan Ann Arbor MI 48109 USA Department of Pathology University of Michigan Ann Arbor MI 48109 USA Department of Emergency Medicine Medical School University of Michigan Ann Arbor MI 48109 USA Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann Arbor MI 48109 USA

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

关键词： Machine learning drug–target interaction prediction DTI software DTI database

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Shaping Integrity: Why Generative Artificial Intelligence Does Not Have to Undermine Education

arXiv

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arXiv 2024年

作者： Tan, Myles Joshua Toledo Maravilla, Nicholle Mae Amor Tan Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL32611 United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL32610 United States Biology Program College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Yo-Vivo Corporation Negros Occidental Bacolod City6100 Philippines

This paper examines the role of generative artificial intelligence (GAI) inpromoting academic integrity within educational settings. It explores how AIcan be ethically integrated into classrooms to enhance learning experiences,foster intrinsic motivation, and support voluntary behavior change amongstudents. By analyzing established ethical frameworks and educational theoriessuch as deontological ethics, consequentialism, constructivist learning, andSelf-Determination Theory (SDT), the paper argues that GAI, when usedresponsibly, can enhance digital literacy, encourage genuine knowledgeconstruction, and uphold ethical standards in education. This researchhighlights the potential of GAI to create enriching, personalized learningenvironments that prepare students to navigate the complexities of the modernworld ethically and effectively. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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BanditPAM: Almost linear time k-medoids clustering via multi-armed bandits 34

BanditPAM: Almost linear time k-medoids clustering via multi...

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34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Tiwari, Mo Zhang, Martin Jinye Mayclin, James Thrun, Sebastian Piech, Chris Shomorony, Ilan Department of Computer Science Stanford University United States Department of Epidemiology Harvard T.H. Chan School of Public Health Electrical and Computer Engineering University of Illinois Urbana-Champaign United States

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means clustering, k-medoids clustering requires the cluster centers to be actual data points and supports arbitrary distance metrics, which permits greater interpretability and the clustering of structured objects. Current state-of-the-art k-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size n for each iteration, being prohibitively expensive for large datasets. We propose BanditPAM, a randomized algorithm inspired by techniques from multi-armed bandits, that reduces the complexity of each PAM iteration from O(n2) to O(n log n) and returns the same results with high probability, under assumptions on the data that often hold in practice. As such, BanditPAM matches state-of-the-art clustering loss while reaching solutions much faster. We empirically validate our results on several large real-world datasets, including a coding exercise submissions dataset from ***, the 10x Genomics 68k PBMC single-cell RNA sequencing dataset, and the MNIST handwritten digits dataset. In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations. The improvements demonstrated by BanditPAM enable k-medoids clustering on a wide range of applications, including identifying cell types in large-scale single-cell data and providing scalable feedback for students learning computer science online. We also release highly optimized Python and C++ implementations of our algorithm1 © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Genome

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Robust Multimodal Cough and Speech Detection using Wearables: A Preliminary Analysis

Robust Multimodal Cough and Speech Detection using Wearables...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yuhan Chen Jeffrey Barahona Iype Eldho Yanbing Yu Riyaz Muhammad Bill Kutsche Michelle L. Hernandez Delesha Carpenter Alper Bozkurt Edgar Lobaton Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA Murata Electronics North America Inc. Smyrna GA USA Department of Pediatrics School of Medicine University of North Carolina at Chapel Hill Chapel Hill NC USA Division of Pharmaceutical Outcomes and Policy Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel Hill NC USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cough detection is a crucial tool for long-term monitoring of respiratory illnesses. While clinical methods are accurate, they are not available in a home-based setting. In contrast, wearable devices offer a more accessible alternative, but face challenges in ensuring user speech privacy and detecting coughs accurately in real-world settings due to potential poor audio quality and background noise. This study addresses these challenges by developing a small-size multimodal cough and speech detection system, enhanced with an Out-of-Distribution (OOD) detection algorithm. Through our analyses, we demonstrate that combining transfer learning, a multimodal approach, and OOD detection techniques significantly improves system performance. Without OOD inputs, the system shows high accuracies of 92.59% in the in-subject setting and 90.79% in the cross-subject setting. With OOD inputs, it still maintains overall accuracies of 91.97% and 90.31% in these respective settings by incorporating OOD detection, despite the number of OOD inputs being twice that of In-Distribution (ID) inputs. This research are promising towards a more efficient, user-friendly cough and speech detection method suitable for wearable devices.

关键词： Voice activity detection Privacy Accuracy System performance Transfer learning Wearable devices engineering in medicine and biology Detection algorithms Monitoring Faces

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